Before projecting a reticle pattern onto a wafer to perform exposure, a semiconductor manufacturing apparatus, such as an exposure apparatus, performs alignment of the wafer with the reticle. To perform the alignment, the semiconductor manufacturing apparatus generally captures a digital image (to be referred to as an “observed image” hereafter) with an alignment mark (to be simply referred to as a “mark” hereafter) on the wafer in the center of the image, using an image input apparatus, such as a CCD camera, and detects the position of the mark from the observed image using a pattern recognition technique. The procedure for detecting the position of the mark is divided into two steps: narrowing down the rough position of the alignment mark from the entire observed image (the first step: this processing step will also be referred to as “pre-alignment” hereafter); and detecting the precise position of the alignment mark at and around the narrowed position (the second step).
In the pre-alignment, the position of the mark is detected by the pattern recognition technique. More specifically, evaluation values are calculated at respective observation positions in the observed image, and one with the highest evaluation value is detected as the position of the mark. In this case, for example, the similarity is evaluated between the feature quantity vector of a template image serving as a reference for detecting the position of the alignment mark and that of an image (to be referred to as a “partial image”) in an observed image obtained by picking up an image of the position of the alignment mark on the wafer and its vicinity, and the pattern of the mark to be recognized is detected on the basis of a partial image with the highest similarity.
If the position of a mark is detected using the above-described technique for pattern recognition in the conventional art (to be referred to as “template image-based recognition” hereafter), degradation in image quality of an observed image to be compared causes a decrease in precision in detecting the feature quantity vector of a partial image, thus resulting in a large decrease in detection rate based on similarity evaluation. In a semiconductor manufacturing apparatus, a change in conditions for a semiconductor manufacturing process, a change in illumination conditions for illuminating a reticle, or the like, may cause the following: (1) a local change in lightness (the degree of brightness) of a mark portion in the observed image; and (2) a local defect in the mark portion in the observed image (e.g., the observed image is partially lost and becomes unfit to undergo similarity evaluation). The phenomena (1) and (2) will be referred to as “local degradation” hereafter. Such local degradation in the conventional art becomes a major factor for a decrease in the detection rate based on similarity evaluation.
An example of a decrease in the detection rate caused by local degradation will be shown below. FIG. 11A shows image T (also to be referred to as a template image or a mark image) to be recognized; FIG. 11B, image B whose lightness has changed in four corners; and FIG. 11C, image C whose four corners are lost.
In FIGS. 11A to 11C, if a pixel value is used as a feature quantity, a process of setting the pixel values of pixels as elements of a feature quantity vector is performed in tandem with scanning each row of pixels, as shown in FIG. 12. FIG. 12 is a view for explaining a step of setting the pixel values of pixels as elements of a feature quantity vector by taking image T of FIG. 11A as an example. First, let a1, b1, a4, and b4 be contours of image T of FIG. 11A. Scanning is performed, pixel by pixel, from a pixel corresponding to a1 at the upper left of image T to the right (the scanning line is denoted by reference numeral 1201). When scanning is performed up to the right end b1, the row to be scanned is moved down by one pixel, and scanning is performed, pixel by pixel, from a pixel corresponding to a left end a2 in a row immediately below the first row to b2 in a direction indicated by scanning line 1202. This operation is sequentially repeated. More specifically, scanning is repeated to a pixel at the right end b4 along a scanning line 1204 in the bottom row. In scanning, the pixel value of each pixel is set as an element of a feature quantity vector.
The pixel value can be set to fall within a dynamic range from 0 to 255. Assume that the pixel value of each pixel in a white portion denoted by reference numeral 1 is 255; that of each pixel in a black portion denoted by reference numeral 2, 0; and that of a gray portion with a tone, halfway between white and black, denoted by reference numeral 3, 128.
For the sake of simplicity, assume that the total number of pixels of each of the images (FIGS. 11A to 11C) is 100; the number of white pixels (pixel value: 255) of image T in FIG. 11A is 50; and the number of black pixels (pixel value: 0) is 50. Also assume that the number of white pixels (pixel value: 255) of image B in FIG. 11B is 35; and the number of black pixels (pixel value: 0) is 65. Further, assume that the number of white pixels (pixel value: 255) of image C in FIG. 11C is 35; the number of black pixels (pixel value: 0) is 35; and the number of gray pixels (pixel value: 128) is 30.
Using the normalized correlation between an image (template image) to be recognized and a partial image in an observed image, the similarity can be given by the following formula (1):Similarity=<(D−μD)·(H−μH)>(|D−μD|·|H−μH|)  (1)                D: the pattern of a template image;        H: the pattern of a partial image:        μD: the average of elements of the feature quantity vector in the template image; and        μH: the average of elements of the feature quantity vector in the partial image.        
In accordance with formula (1), the similarity between image T and image B, and that between image T and image C, are obtained on the basis of the one hundred-dimensional feature quantity vectors of the images, each of whose total number of pixels is 100. The results are as follows:
(1) Similarity between Image T and Image B: 0.734; and
(2) Similarity between Image T and Image C: 0.837.
In the above example, the similarities are obtained on the basis of the one-hundred-dimensional feature quantity vectors, each corresponding to one hundred pixels. A further simplified example, i.e., a case wherein the total number of pixels is five will be explained below. Assume that the feature quantity vector of a template image to be recognized is given as a five-dimensional vector [1, 1, 0, 1, 1]. If the feature quantity vector of a partial image containing a mark portion changes from [1, 1, 0, 1, 1,] to [1, 1, 0, 0, 1] due to local degradation (the feature quantity of the fourth pixel from the left degrades from “1” to “0”), the similarity obtained by formula (1) decreases to 0.61 (in the absence of local degradation, the similarity does not change and remains 1.0).
As shown in the above example, in the evaluation of similarity using the normalized correlation between a template image and a partial image, the similarity largely decreases due to local degradation in the image, thus resulting in a decrease in the precision in detecting the position of a mark in pre-alignment. Examples of a template-based recognition technique include one shown in Japanese Patent Laid-Open No. 2003-203846.
However, in template-based recognition in the conventional art, the similarity in mark portion largely decreases due to local degradation in image data. As a result, if a partial image containing a mark portion to be recognized has a lower similarity than that of a portion except for the mark portion, misrecognition occurs in the position of a mark to be recognized.
In a semiconductor exposure apparatus, when the apparatus stops due to misrecognition of a mark position, an alignment process needs to be retried, to correct the misrecognition, and the availability of the apparatus decreases.
The present invention has as its exemplified object to provide an image processing technique, which suppresses misrecognition of an object region in object image data.